JARVIS Agent Auth

Inspiration

What it does

Inspiration

Running a distributed AI system with 600+ autonomous agents across 3 machines creates a massive security challenge. Each agent needs different levels of access to APIs, databases, and external services. Without proper authorization, any agent could escalate privileges or access sensitive data. Auth0's Token Vault solves this by providing a centralized, secure way to manage agent credentials with fine-grained scopes.

What it does

JARVIS AgentAuth is a production-ready framework for orchestrating multiple AI agents with enterprise-grade security:

• Agent Identity Management: Each AI agent (Claude, Gemini, LM Studio local models) gets a unique Auth0 identity with specific scopes
• Token Vault Integration: Secure storage and rotation of API keys and OAuth tokens through Auth0 Token Vault
• Scope-Based Access Control: Agents can only perform actions within their granted permissions
• Multi-Model Consensus with Auth: Multiple AI models vote on decisions, each authenticating independently — preventing a compromised model from influencing outcomes
• Audit Trail: Every agent action is logged with its Auth0 identity for full traceability
• Step-Up Authentication: High-stakes actions require additional authorization checks

How we built it

• Auth0 Token Vault for agent credential management and OAuth flows
• Auth0 M2M SDK (Python) for machine-to-machine authentication
• LM Studio running 7 local AI models (Qwen3-8B, DeepSeek-R1, Nemotron-3, GLM-4.7-Flash, DeepSeek-Coder, GPT-OSS-20B)
• MCP (Model Context Protocol) with 88 tool handlers, each requiring Auth0 authorization
• SQLite3 for immutable audit logging
• n8n for 20+ workflow automations with Auth0-authenticated triggers
• Python/Node.js backend orchestration across 3 machines with 6 GPUs

Challenges we ran into

• Managing token lifecycle across 600+ agents without performance degradation
• Implementing step-up auth for the multi-consensus voting system
• Ensuring local LM Studio models (which run offline) can still participate in the Auth0 flow via a proxy auth layer
• Handling token refresh across 3 distributed machines with different network conditions

Accomplishments that we're proud of

• Zero hardcoded credentials in the entire 30-repo codebase
• Sub-300ms average authentication latency per agent action
• Complete audit trail for every agent decision across all 3 machines
• Multi-model consensus where each AI independently authenticates — no single point of compromise

What we learned

• Agent authorization is fundamentally different from user authorization — agents need much more granular, dynamic scopes
• Token Vault dramatically simplifies credential management at scale
• The security model for multi-agent systems needs to account for adversarial agents, not just external threats

What's next for JARVIS AgentAuth

• Implement Auth0 Fine-Grained Authorization (FGA) for relationship-based access control between agents
• Add real-time anomaly detection on agent auth patterns
• Open-source the Auth0 agent middleware as an npm/pip package
• Scale to 1000+ agents with hierarchical authorization ## How we built it

Challenges we ran into

Accomplishments that we're proud of

What we learned

What's next for JARVIS AgentAuth

Built With

• auth0
• docker
• gpu
• jwt
• n8n
• node.js
• oauth
• python
• sqlite